TAI-SHUAN LAITsao, Hsiao-MeiHsiao-MeiTsaoYU-HSIANG CHOULiang, Shu-LingShu-LingLiangKUO-LIONG CHIENYUNG-MING CHEN2024-03-052024-03-052024-070929-6646https://scholars.lib.ntu.edu.tw/handle/123456789/640266Predictive modeling aids in identifying patients at high risk of adverse events. Using routinely collected data, we report a competing risk prediction model for kidney failure.enChronic; Chronic kidney disease; Clinical decision rules; Kidney failurejournal article10.1016/j.jfma.2023.11.010380442102-s2.0-85178598632https://api.elsevier.com/content/abstract/scopus_id/85178598632